Object Tracking (Computer Vision)

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.

We present a novel solution to the problem of recovering and tracking the 3D position, orientation and full articulation of a human hand from markerless visual observations obtained by a Kinect sensor. We treat this as an optimization problem, seeking for the hand model parameters that minimize the discrepancy between the appearance
and 3D structure of hypothesized instances of a hand model and actual hand observations. This optimization problem is effectively solved using a variant of Particle Swarm
Optimization (PSO). The proposed method does not require special markers and/or a complex image acquisition setup. Being model based, it provides continuous solutions
to the problem of tracking hand articulations. Extensive experiments with a prototype GPU-based implementation of the proposed method demonstrate that accurate and ro-
bust 3D tracking of hand articulations can be achieved in near real-time (15Hz).

We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains the RGB-D observations provided by a Kinect sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible.

We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Atten-tional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robust-ness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.

A crucial prerequisite for recycling forming an integral part of municipal solid waste (MSW) management is sorting of useful materials from source-separated MSW. Researchers have been exploring automated sorting techniques to improve the overall efficiency of recycling process. This paper reviews recent advances in physical processes, sensors, and actuators used as well as control and autonomy related issues in the area of automated sorting and recycling of source-separated MSW. We believe that this paper will provide a comprehensive overview of the state of the art and will help future system designers in the area. In this paper, we also present research challenges in the field of automated waste sorting and recycling.

We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encoder is selected for a given target, and only this auto-encoder is used. To achieve high tracking performance with the compressed feature map, we introduce extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert auto-encoders. We validate the proposed context-aware framework through a number of experiments, where our method achieves a comparable performance to state-of-the-art track-ers which cannot run in real-time, while running at a significantly fast speed of over 100 fps.

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

This work considers the classification of power quality disturbances based on VMD (Variational Mode Decomposition) and
EWT (Empirical Wavelet Transform) using SVM (Support Vector Machine). Performance comparison of VMD over EWT is
done for producing feature vectors that can extract salient and unique nature of these disturbances. In this paper, these two
adaptive signal processing methods are used to produce three Intrinsic Mode Function (IMF) components of power quality
signals. Feature vectors produced by finding sines and cosines of statistical parameter vector of three different IMF candidates
are used for training SVM. Validation for six different classes of power qualities including normal sinusoidal signal, sag, swell,
harmonics, sag with harmonics, swell with harmonics is performed using synthetic data in MATLAB. Classification results using
SVM shows that VMD outperforms over EWT for feature extraction process and the classification accuracy is tabled.

We present a novel probabilistic framework that jointly models individuals and groups for tracking. Managing groups is
challenging, primarily because of their nonlinear dynamics and complex layout which lead to repeated splitting and merging events.
The proposed approach assumes a tight relation of mutual support between the modeling of individuals and groups, promoting the
idea that groups are better modeled if individuals are considered and vice versa. This concept is translated in a mathematical model
using a decentralized particle filtering framework which deals with a joint individual-group state space. The model factorizes the joint
space into two dependent subspaces, where individuals and groups share the knowledge of the joint individual-group distribution.
The assignment of people to the different groups (and thus group initialization, split and merge) is implemented by two alternative
strategies: using classifiers trained beforehand on statistics of group configurations, and through online learning of a Dirichlet process
mixture model, assuming that no training data is available before tracking. These strategies lead to two different methods that can be
used on top of any person detector (simulated using the ground truth in our experiments). We provide convincing results on two recent
challenging tracking benchmarks.

Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers’ initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian tracking benchmark datasets, our formulation attains accuracy that is comparable to, or better than, the state-of-the-art pedestrian trackers that must exploit calibration information and operate offline.

In vitro motility assays, in which fluorescently labeled actin filaments are propelled by myosin molecules adhered to a glass coverslip, require that actin filament velocity be determined. We have developed a computer-assisted filament tracking system that reduced the analysis time, minimized investigator bias, and provided greater accuracy in locating actin filaments in video images. The tracking routine successfully tracked filaments under experimental conditions where filament density, size, and extent of photobleaching varied dramatically. Videotaped images of actin filament motility were digitized and processed to enhance filament image contrast relative to background. Once processed, filament images were cross correlated between frames and a filament path was determined. The changes in filament centroid or center position between video frames were then used to calculate filament velocity. The tracking routine performance was evaluated and the sources of noise that contributed to errors in velocity were identified and quantified. Errors originated in algorithms for filament centroid determination and in the choice of sampling interval between video frames. With knowledge of these error sources, the investigator can maximize the accuracy of the velocity calculation through access to user-definable computer program parameters.

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.

—The kernelized correlation filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an output constraint transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art track-ers. To encourage further developments, the source code is made available.

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence "re-illumination" algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases).

— In this paper, we propose a new tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The resulting filter jointly tracks the kinematic and shape parameters of the target and is called the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to have a computationally tractable filter. In addition, a new method that is consistent with the applied clustering method is embedded into the filter recursions in order to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the robust and effective performance of the proposed filter. Furthermore, real data collected from a UWB sensor network are used to assess the performance of the proposed filter. It is shown that the proposed filter yields a very promising performance in estimation of the kinematic and shape parameters of the target.

The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illumination manifold-based normalization; (ii) pose robustness is achieved by decomposing each appearance manifold into semantic Gaussian pose clusters, comparing the corresponding clusters and fusing the results using an RBF network; (iii) a fully automatic recognition system based on the proposed method is described and extensively evaluated on 600 head motion video sequences with extreme illumination, pose and motion pattern variation. On this challenging data set our system consistently demonstrated a very high recognition rate (95% on average), significantly outperforming state-of-the-art methods from the literature.

It is much easier to divide attention across the left and right visual hemifields than within the same visual hemifield. Here we investigate whether this benefit of dividing attention across separate visual fields is evident at early cortical processing stages. We measured the steady-state visual evoked potential, an oscillatory response of the visual cortex elicited by flickering stimuli, of moving targets and distractors while human observers performed a tracking task. The amplitude of responses at the target frequencies was larger than that of the distractor frequencies when participants tracked two targets in separate hemifields, indicating that attention can modulate early visual processing when it is divided across hemifields. However, these attentional modulations disappeared when both targets were tracked within the same hemifield. These effects were not due to differences in task performance, because accuracy was matched across the tracking conditions by adjusting target sp...

BACKGROUND: Liver injuries induced by carbon tetrachloride are the best-characterized system of xenobiotic-induced hepatotoxicity and commonly used model for the screening of hepatoprotective activities of drugs. The present study evaluates the hepatoprotective activity of sea buckthorn (Hippophae rhamnoides L.), family Elaeagnaceae, on carbon tetrachloride (CCl4)-induced liver injury in male albino rats. The study was performed on Sprague–Dawley male albino rats weighing about 180–200 g. The animals were pretreated with three different doses of leaf extract (50, 100 and 200 mg kg−1 body weight) for 5 days. Hepatotoxicity was induced by single oral administration of 1.5 mL CCl4 kg−1 body weight on the fifth day. The animals were then sacrificed and assessed for various biochemical parameters.RESULTS: Administration of CCl4 significantly enhanced glutamate oxaloacetate transferase (GOT), glutamate pyruvate transferase (GPT), alkaline phosphatase (ALP) and bilirubin, and decreased total protein levels in the serum. Treatment with CCl4 also significantly decreased reduced glutathione (GSH), and decreased glutathione peroxidase and superoxide dismutase activity. CCl4 treatment also caused a significant increase in hepatic lipid peroxidation as assessed by malondialdehyde (MDA) levels in the tissue. Pretreatment of leaf extract at a concentration of 100 and 200 mg kg−1 body weight significantly (P < 0.05) protected the animals from CCl4-induced liver injury. The extract significantly restricted the CCl4-induced increase of GOT, GPT, ALP and bilirubin and better maintained protein levels in the serum. Further, it also enhanced GSH and decreased MDA levels.CONCLUSION: The results show that sea buckthorn leaf extract has significant hepatoprotective effects which might be due to its antioxidant activity and can be developed as a nutraceutical or food supplement against liver diseases. Copyright © 2008 Society of Chemical Industry

This paper addresses the task of time-separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several major novelties: (i) unlike previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how image space local, pair-wise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed; (iv) lastly, we introduce a new and, to the best of our knowledge, the only data corpus suitable for the evaluation of set-based aerial image registration algorithms. Using this data set, we demonstrate: (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task; and (ii) that the increase in the number of available images in a set consistently reduces the average registration error, with a major difference already for a single additional image.

This work presents a system to perform autonomous landing of a small size fixed-wing Unmanned Aerial Vehicle (UAV) on a Fast Patrol Boat (FPB). We propose a ground-based vision system with the camera, image capture and processing equipment installed in the ship, thus reducing the UAV size, weight and power requirements. The system observes the UAV and computes the control commands to send to the UAV via radio. This approach makes it also possible to use standard UAVs equipped with commercial autopilots. The developed system uses the captured image as input and a Particle Filter (PF) structure to estimate the UAV trajectory. It is also used an Unscented Kalman Filter (UKF) for the translational motion filtering and an Unscented Bingham Filter (UBiF) for the rotational motion filtering. This filtering structure is reminiscent of the Unscented Particle Filter (UPF). The obtained tracking error is compatible with automatic landing requirements.

Illumination invariance remains one of the most researched, yet the most challenging aspect of automatic face recognition. In this paper the discriminative power of colour-based invariants is investigated in the presence of large illumination changes between training and query data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) a general photometric model of the camera is described and it is shown how its parameters can be estimated from realistic video input of pseudo-random head motion, (ii) several novel colour-based face invariants are derived for different special instances of the camera model, and (iii) the performance of the largest number of colour-based representations in the literature is evaluated and analysed on a database of 700 video sequences. The reported results suggest that: (i) colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images in extreme illuminations, and (ii) that the non-linearities of the general photometric camera model have a significant effect on recognition performance. This highlights the limitations of previous work and emphasizes the need to assess face recognition performance using training and query data which had been captured by different acquisition equipment.

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenological models. Scarcer yet is the application of said models in the practical domain. The present paper presents a framework which allows resistance training practitioners (coaches or athletes themselves) to employ a recently proposed model of neuromuscular engagement and adaptation in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to infer the value of the parameters of the underlying neuromuscular model. This is achieved by using known differential equations describing the motion of the resistance during a particular exercise, in what is usually termed the inverse dynamics problem. Lastly, a computer simulation of hypothetical training bouts, using athlete specific capability parameters, is used to predict the effected adaptation and with it the changes in performance. The software developed and for the first time described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop. By bridging the gap between the theoretical and applied aspects of sports science, the present contribution serves to strongly encourage a further research focus on the development of more general, comprehensive models in this field.

Conventional tracking solutions are not able to deal with abrupt motion as these are based on a smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. We address this problem by casting tracking as an optimisation problem and propose a novel abrupt motion tracker based on swarm intelligence – the SwATrack. Unlike existing swarm-based filtering methods, we first of all introduce an optimised swarm-based sampling strategy for a tradeoff between the exploration and exploitation of the state space in search for the optimal proposal distribu- tion. Secondly, we propose Dynamic Acceleration Parameters (DAP) that allow on the fly tuning of the best mean and variance of the distribution for sampling. Combining the two strategies within the Particle Swarm Optimisation framework represents a novel method to address abrupt motion. To the best of our knowledge, this has never been done before. Thirdly, we introduce a new dataset – the Malaya Abrupt Motion (MAMo) dataset that consists of 12 videos with groundtruth. Finally, experimental on both quantitative and qualitative results have shown the effectiveness of the proposed method in terms of dataset unbiased, object size invariant and fast recovery in tracking the abrupt motions.

Monitoring coral reef populations as part of environmental assessment is essential. Recently, many marine science researchers are employing low-cost and power efficient Autonomous Underwater Vehicles (AUV) to survey coral reefs. While the counting problem, in general, has rich literature, little work has focused on estimating the density of coral population using AUVs. This paper proposes a novel approach to identify, count, and estimate coral populations. A Convolutional Neural Network (CNN) is utilized to detect and identify the different corals, and a tracking mechanism provides a total count for each coral species per transect. Experimental results from an Aqua2 underwater robot and a stereo hand-held camera validated the proposed approach for different image qualities.

The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z]E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals' directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving -- object representation is extracted from an image in only 6%  of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%.